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import gradio as gr
import numpy as np
import pickle
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression, Perceptron
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# Load or train models
try:
with open("scaler.pkl", "rb") as scaler_file:
scaler = pickle.load(scaler_file)
with open("logistic_regression.pkl", "rb") as model_file:
logistic_regression = pickle.load(model_file)
with open("perceptron.pkl", "rb") as model_file:
perceptron = pickle.load(model_file)
except FileNotFoundError:
print("Training models...")
df = pd.read_excel("Student-Employability-Datasets.xlsx", sheet_name="Data")
X = df.iloc[:, 1:-2].values
y = (df["CLASS"] == "Employable").astype(int)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
logistic_regression = LogisticRegression(random_state=42)
logistic_regression.fit(X_train_scaled, y_train)
perceptron = Perceptron(random_state=42)
perceptron.fit(X_train_scaled, y_train)
with open("scaler.pkl", "wb") as scaler_file:
pickle.dump(scaler, scaler_file)
with open("logistic_regression.pkl", "wb") as model_file:
pickle.dump(logistic_regression, model_file)
with open("perceptron.pkl", "wb") as model_file:
pickle.dump(perceptron, model_file)
# Prediction function
def predict_employability(name, ga, mos, pc, ma, sc, api, cs, model_choice):
if not name:
name = "The candidate"
input_data = np.array([[ga, mos, pc, ma, sc, api, cs]])
input_scaled = scaler.transform(input_data)
if model_choice == "Logistic Regression":
prediction = logistic_regression.predict(input_scaled)
else:
prediction = perceptron.predict(input_scaled)
if prediction[0] == 1:
return f"{name} is Employable 😊"
else:
return f"{name} is Less Employable - Work Hard! 💪"
# Gradio UI
with gr.Blocks() as app:
gr.Markdown("# Employability Evaluation 🚀")
with gr.Row():
with gr.Column():
name = gr.Textbox(label="Name")
ga = gr.Slider(1, 5, step=1, label="General Appearance")
mos = gr.Slider(1, 5, step=1, label="Manner of Speaking")
pc = gr.Slider(1, 5, step=1, label="Physical Condition")
ma = gr.Slider(1, 5, step=1, label="Mental Alertness")
sc = gr.Slider(1, 5, step=1, label="Self Confidence")
api = gr.Slider(1, 5, step=1, label="Ability to Present Ideas")
cs = gr.Slider(1, 5, step=1, label="Communication Skills")
model_choice = gr.Radio(["Logistic Regression", "Perceptron"], label="Select Model")
predict_btn = gr.Button("Get Evaluation")
with gr.Column():
result_output = gr.Textbox(label="Employability Prediction")
# Button Click Event
predict_btn.click(
fn=predict_employability,
inputs=[name, ga, mos, pc, ma, sc, api, cs, model_choice],
outputs=[result_output]
)
# Launch the app
app.launch(share=True) give me the module name and the requirements.txt |